A multi-scale parallel MRI reconstruction model based on a variational model and Transformer (VNTM) was proposed, to enhance the quality of reconstructed MR images from undersampled multi-coil MR data. First, undersampled multi-coil k-space data were used to estimate sensitivity maps, with an intermediate-stage enhancement strategy applied to improve the accuracy of these maps. Next, the undersampled multi-coil k-space data and estimated sensitivity maps were input into a variational model for reconstruction. In the variational model, resolution was reduced through a pre-processing module to reduce computational load; multi-scale features were then effectively fused through a multi-scale U-shaped network with the Transformer. Finally, a post-processing module was applied to restore resolution, and data consistency operations were performed on the output to ensure fidelity. Extensive quantitative and qualitative experiments were conducted on publicly available datasets to validate the effectiveness of the proposed method. The experimental results indicate that the proposed reconstruction model achieves superior reconstruction quality and more stable performance in terms of peak signal-to-noise ratio, structural similarity, and visual effects. In addition, a series of ablation studies and robustness evaluations with varying auto-calibration signal (ACS) region sizes were carried out, confirming that VNTM maintained consistently high reconstruction performance under diverse conditions.